College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
Photodiagnosis Photodyn Ther. 2021 Jun;34:102313. doi: 10.1016/j.pdpdt.2021.102313. Epub 2021 Apr 26.
Chronic renal failure (CRF) is a disease with a high morbidity rate that can develop into uraemia, resulting in a series of complications, such as dyspnoea, mental disorders, hypertension, and heart failure. CRF may be controlled clinically by drug intervention. Therefore, early diagnosis and control of the disease are of great significance for the treatment and prevention of chronic renal failure. Based on the complexity of CRF diagnosis, this study aims to explore a new rapid and noninvasive diagnostic method.
In this experiment, the serum Raman spectra of samples from 47 patients with CRF and 53 normal subjects were obtained. In this study, Serum Raman spectra of healthy and CRF patients were identified by a Convolutional Neural Network (CNN) and compared with the results of identified by an Improved AlexNet. In addition, different amplitude of noise were added to the spectral data of the samples to explore the influence of a small random noise on the experimental results.
A CNN and an Improved AlexNet was used to classify the spectra, and the accuracy was 79.44 % and 95.22 % respectively. And the addition of noise did not significantly interfere with the classification accuracy.
The accuracy of CNN of this study can be as high as 95.22 %, which greatly improves its accuracy and reliability, compared to 89.7 % in the previous study. The results of this study show that the combination of serum Raman spectrum and CNN can be used in the diagnosis of CRF, and small random noise will not cause serious interference to the data analysis results.
慢性肾衰竭（CRF）是一种发病率较高的疾病，可发展为尿毒症，导致一系列并发症，如呼吸困难、精神障碍、高血压和心力衰竭。CRF 可以通过药物干预进行临床控制。因此，早期诊断和控制疾病对于治疗和预防慢性肾衰竭具有重要意义。基于 CRF 诊断的复杂性，本研究旨在探索一种新的快速、无创诊断方法。
本实验采集了 47 例 CRF 患者和 53 例正常对照者的血清拉曼光谱。本研究采用卷积神经网络（CNN）对健康人和 CRF 患者的血清拉曼光谱进行识别，并与改进的 AlexNet 的识别结果进行比较。此外，还向样本光谱数据中添加不同幅度的噪声，以探讨小随机噪声对实验结果的影响。
使用 CNN 和改进的 AlexNet 对光谱进行分类，准确率分别为 79.44%和 95.22%。并且添加噪声不会对分类准确率产生显著干扰。
本研究中 CNN 的准确率高达 95.22%，与之前研究的 89.7%相比，大大提高了其准确性和可靠性。本研究结果表明，血清拉曼光谱结合 CNN 可用于 CRF 的诊断，小的随机噪声不会对数据分析结果造成严重干扰。
